M
Mouldi Bedda
Researcher at Al Jouf University
Publications - 20
Citations - 334
Mouldi Bedda is an academic researcher from Al Jouf University. The author has contributed to research in topics: Support vector machine & Speaker recognition. The author has an hindex of 6, co-authored 20 publications receiving 233 citations. Previous affiliations of Mouldi Bedda include University of Alabama.
Papers
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Journal ArticleDOI
A novel active learning method using SVM for text classification
TL;DR: Experimental results indicate that the proposed active learning method significantly reduces the labeling effort, while simultaneously enhancing the classification accuracy.
Proceedings ArticleDOI
Improved tree model for arabic speech recognition
Nacereddine Hammami,Mouldi Bedda +1 more
TL;DR: A fast learning method for a graphical probabilistic model for discrete speech recognition based on spoken Arabic digit recognition by means of a new proposed spanning tree structure that takes advantage of the temporal nature of speech signal is introduced.
Proceedings ArticleDOI
Handwritten Arabic character recognition based on SVM Classifier
TL;DR: A novel algorithm for smoothing image and segmentation of the Arabic character using width writing estimated from skeleton character and Principal component Analysis (PCA) as data processing algorithm to features vector in order to reduce dimension is proposed.
Proceedings ArticleDOI
The second-order derivatives of MFCC for improving spoken Arabic digits recognition using Tree distributions approximation model and HMMs
TL;DR: The system was developed using the Hidden Markov Models (HMMs) and Tree distribution approximation model and was able to reach an overall recognition accuracy of 98.41%, which is satisfactory compared to previous work on spoken Arabic digits speech recognition.
Journal ArticleDOI
Segmentation and Recognition of Handwritten Numeric Chains
TL;DR: This study proposes an off line system for the recognition of the handwritten numeric chains based mainly on the evaluation of neural network performances, trained with the gradient back propagation algorithm.